On natural language text collections, finding the k documents most relevant to a query is generally solved with inverted indexes. On general string collections, however, more sophisticated data structures are necessary. Navarro and Nekrich [SODA 2012] showed that a linear-space index can solve such top-k queries in optimal time O(m +k), where m is the query length. Konow and Navarro [DCC 2013] implemented the scheme, managing to solve top-k queries within microseconds with an index using 3.3-4.0 bytes per character (this includes the storage of the collection itself). In this paper we introduce a new implementation using significantly less space, 2.5-3.0 bytes per character (again, including the collection), and retaining similar query times. For short queries, which are the most difficult, our new index actually outperforms the previous one, as well as all the other solutions in the literature. We also show that our index can be built on very large text collections, and that it can handle phrase queries efficiently on natural language text collections. In the latter case, it uses about the same space of the tokenized text (and replaces it), while answering phrase queries an order of magnitude faster than a positional inverted index.
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